R中tapply和aggregate的区别是什么？

Question

Aaa <- data.frame(amount=c(1,2,1,2,1,1,2,2,1,1,1,2,2,2,1), 
                  card=c("a","b","c","a","c","b","a","c","b","a","b","c","a","c","a"))

aggregate(x=Aaa$amount, by=list(Aaa$card), FUN=mean)

##   Group.1    x
## 1       a 1.50
## 2       b 1.25
## 3       c 1.60

tapply(Aaa$amount, Aaa$card, mean)

##    a    b    c 
## 1.50 1.25 1.60 

以上是示例代码.

看来,aggregate和tapply两者都非常方便,并执行类似的功能.

有人可以解释或举例说明他们之间的差异吗？

Answer 1

aggregate设计用于处理具有一个函数的多个列,并为每个类别返回一行数据帧,而tapply设计用于处理单个向量,其结果作为矩阵或数组返回.仅使用双列矩阵并不能真正实现任一功能(或其显着差异)的容量.aggregate也有一个公式方法,tapply但没有.

> Aaa <- data.frame(amount=c(1,2,1,2,1,1,2,2,1,1,1,2,2,2,1), cat=sample(letters[21:24], 15,rep=TRUE),
+                   card=c("a","b","c","a","c","b","a","c","b","a","b","c","a","c","a"))
> with( Aaa, tapply(amount, INDEX=list(cat,card), mean) )
    a   b   c
u 1.5 1.5  NA
v 2.0 1.0 2.0
w 1.0  NA 1.5
x 1.5  NA 1.5

>  aggregate(amount~cat+card, data=Aaa, FUN= mean) 
  cat card amount
1   u    a    1.5
2   v    a    2.0
3   w    a    1.0
4   x    a    1.5
5   u    b    1.5
6   v    b    1.0
7   v    c    2.0
8   w    c    1.5
9   x    c    1.5

该xtabs功能还提供R"表",并具有公式界面.R表是通常具有整数值的矩阵,因为它们被设计为"列联表",其中包含边际类别的交叉分类中的项目计数.